The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Facial emotion recognition in modern distant education system using SVM
Abstract
Network and multimedia is the trend of the development of the modern education technology. The space-time separation of the modern distance education leads to the affection deficiency of the learners. In order to estimate the affective state of the learners in the modern distant education system, the model of distance education system based on affective computing is proposed and realized which took both facial features as input data. A real time facial expression recognition based on support vector machine (SVM) algorithm is also applied in the system. After test of the algorithm in the experiment, 100 facial images of 15 learners are randomly selected from database including six basic emotions and neutral as a training set. 265 facial images of 42 persons are used as testing set. The total recognition rate is 84.55%.
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“an ethical concern may arise from a real-time facial expression recognition system used to predict the affective state (e.g. Jian-Ming Sun et al., 2008) or attendance of the learners without their consent (e.g. Pattnaik & Mohanty 2020).”
From Ethical principles for artificial intelligence in education (2023) · cites this paper as Jian-Ming Sun et al., 2008
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Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
Research works printed but not linked: 8
- 10.1016/S1071-5819(03)00052-1 · DOI · Paper page
- 10.1109/ICIP.1995.529584 · DOI
- yao, 2006, current network-based distance education practice in china, Proc 6th Int Conf Information Technology Based Higher Education and Training, 613
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- vapnik, 1998, Statistical Learning Theory
- 10.1109/ICCV.2003.1238452 · DOI
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Cited by
Sorted by citations
Ethical principles for artificial intelligence in education
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11316-w · Cited by 1,228
“For instance, an ethical concern may arise from a real-time facial expression recognition system used to pre - dict the affective state (e.g. Jian-Ming Sun et al., 2008 ) or attendance of the learners without their consent (e.g. Pattnaik & Mohanty 2020 ).”
BackgroundArtificial Intelligence Techniques for Distance Education: A Systematic Literature Review
Aljarrah, A., Ababneh, M., Karagozlu, D., & Ozdamli, F. (2021) · TEM Journal · doi:10.18421/tem104-18 · Cited by 23
Metadata
- Authors
- Sun, J.-M., Pei, X.-S., & Zhou, S.-S.
- Affiliations
- Xi'an University of Technology (CN)
- Henan University of Science and Technology (CN)
- ORCID
- Jian-Ming Sun
- Venue
- 2008 International Conference on Machine Learning and Cybernetics
- Publisher
- IEEE
- Published
- 2008-07
- Pages
- 3545-3548
- OpenAlex
- W2127725212
- Cited by
- 28
- References
- 1
- Citations by year
- 2026: 3 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 7 · 2020: 4 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 1 · 2015: 2
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- facial emotion recognition, affective computing, distance education, facial expression recognition, support vector machine, basic emotions, real-time emotion recognition, facial features
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- CoChat. (2026). Citation check: Facial emotion recognition in modern distant education system using SVM. https://app.cochat.ai/papers/10.1109/icmlc.2008.4621018
- The paper
- Sun, J.-M., Pei, X.-S., & Zhou, S.-S. (2008). Facial emotion recognition in modern distant education system using SVM. 2008 International Conference on Machine Learning and Cybernetics. https://doi.org/10.1109/icmlc.2008.4621018
